Senior MLOps Engineer

Key Responsibilities:

• Design, build, and optimize ML and data pipelines for LLM applications.
• Collaborate with data scientists and software engineers to deploy scalable, production-ready models.
• Implement fine-tuning techniques for large language models to enhance performance.
• Utilize cloud platforms (AWS, Azure, GCP) for deploying machine learning solutions.
• Manage MLOps technologies and tools, including but not limited to AWS SageMaker, Kubeflow, and Vertex AI.
• Automate workflows and streamline processes using scripting and CI/CD tools.
• Ensure adherence to best practices in software development, including test-driven development and version control.
• Maintain documentation and facilitate effective communication across teams using tools like Jira and Confluence.

Qualifications:

• A minimum of 5 years of experience in Data Science, with at least 2 years in Data Engineering, ML Engineering, or MLOps.
• Proficiency in programming languages such as Python and R.
• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch) and data visualization tools (e.g., Tableau).

What we offer:

• Involvement in dynamic and interesting projects.
• A collaborative and supportive team culture.
• A flexible work environment.
• A comprehensive benefits package tailored to your preferences.

Who you are / What you’ll do

Senior MLOps Engineer with a strong background in machine learning and data science, with experience in developing and deploying applications that leverage large language models (LLMs), focusing on agentic design and automation within the MLOps pipeline.

Tech stack / Team description

Cloud platforms (AWS, Azure, GCP); MLOps tools including AWS SageMaker, Kubeflow, and Vertex AI; scripting and CI/CD; Jira and Confluence for documentation and communication.